Importing All Necessary Libraries



Importing Dataset



Data Cleaning


Removing First Unnamed Column

Checking Null Values

Removing Null Values

Checking Null Values After Removing


Data Exploration


Number of Rows and Columns

Datatype of Each Columns

Describe Dataset

Count of Age of Reviewer

Number of Customer's Positive and Negative Recommendation

Number of Different Divisions

Number of Different Department

Number of Different Classes

Number of Customer's Rating from 1 to 5

Number of Positive Feedbacks


Data Preprocessing


Before Processing

Initializing Classes for Text Preparation

Processing Title Column

Processing Review Text Column

After Processing

Frequent Words in Positive Recommendation

Frequent Words in Negative Recommendation


1. Predicting using only Review Text


Selecting Columns

Splitting Dataset into 80% Training Set and 20% Testing Set

Converting into Vectorize Form

Naive Bayes Multinomial

Classification Report and Confusion Matrix


2. Predicting using only Review Text where Positive Feedback Count is greater than 1


Selecting Columns

Splitting Dataset into 80% Training Set and 20% Testing Set

Converting into Vectorize Form

Naive Bayes Multinomial

Classification Report and Confusion Matrix


3. Predicting using only Title


Selecting Columns

Splitting Dataset into 80% Training Set and 20% Testing Set

Converting into Vectorize Form

Naive Bayes Multinomial

Classification Report and Confusion Matrix


4. Predicting using only Title where Positive Feedback Count is greater than 1


Selecting Columns

Splitting Dataset into 80% Training Set and 20% Testing Set

Converting into Vectorize Form

Naive Bayes Multinomial

Classification Report and Confusion Matrix


5. Predicting using Recommended IND and Positive Feedback Count


Selecting Columns

Splitting Dataset into 80% Training Set and 20% Testing Set

Converting into Vectorize Form

Naive Bayes Multinomial

Classification Report and Confusion Matrix

Process

  1. Predicting using only Review Text

  2. Predicting using only Review Text where Positive Feedback Count is greater than 1

  3. Predicting using only Title

  4. Predicting using only Title where Positive Feedback Count is greater than 1

  5. Predicting using Rating and Positive Feedback Count